Evidence map›Paper›PMID 36760710›Full record

ReviewFrontiers in aging neuroscience2022

Mitigating effects and mechanisms of Tai Chi on mild cognitive impairment in the elderly.

Xin Wang, Keyi Si, Wei Gu, Xueqiang Wang

Open access · goldAbstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Frontiers in aging neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 5 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed, 5 pooled it
1.9field-weighted citation impact, top 14% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

13 citing papers in PubMed, 5 syntheses or guidelines pooled it, 14 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors at 2 institutions in 1 country.

Xin WangFaculty of Traditional Chinese Medicine, Naval Medical University, Shanghai, China.
Keyi SiDepartment of Military Health Statistics, Naval Medical University, Shanghai, China.
Wei GuFaculty of Traditional Chinese Medicine, Naval Medical University, Shanghai, China.
Xueqiang WangDepartment of Sport Rehabilitation, Shanghai University of Sport, Shanghai, China.
Shanghai University of Sport · CNSecond Military Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Mild cognitive impairment (MCI) is a major public health concern that endangers health and decreases the quality of life of the elderly around the world. A recent clinical guideline has recommended regular exercise (twice per week) for patients with MCI as part of an overall approach to management. Tai Chi, a form of light-to-moderate-intensity mind-body exercise, is particularly suitable for seniors. This review aims to summarize epidemiological studies related to the effects of Tai Chi on symptom remission in older adults with MCI and reveal the potential mechanisms. Evidence suggested that Tai Chi can improve cognitive functions and alleviate the accompanying symptoms of MCI in the elderly potentially by activating the expression of signals in different brain regions, altering their connectivity, increasing the brain volume, and modulating brain-derived neurotropic and inflammation factors. Studies comparing various types of Tai Chi may contribute to the identification of paradigms that have appropriate intensities and difficulty and exert good effects on older people with MCI. In addition, studies are warranted to determine the frequency and duration of training that can optimize the beneficial effects of Tai Chi on MCI.

Indexed as

cognitive functionelderlymechanismmild cognitive impairmentTai Chi

Identifiers

PMID36760710
PMCPMC9906996
OpenAlexW4319812251

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.